Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features
In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6517 |
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| author | Lal Hussain Hadeel Alsolai Siwar Ben Haj Hassine Mohamed K. Nour Mesfer Al Duhayyim Anwer Mustafa Hilal Ahmed S. Salama Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah |
| author_facet | Lal Hussain Hadeel Alsolai Siwar Ben Haj Hassine Mohamed K. Nour Mesfer Al Duhayyim Anwer Mustafa Hilal Ahmed S. Salama Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah |
| author_sort | Lal Hussain |
| collection | DOAJ |
| description | In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate. |
| first_indexed | 2024-03-09T22:07:07Z |
| format | Article |
| id | doaj.art-0f427258500940e89cf5ec71b11aebda |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:07:07Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0f427258500940e89cf5ec71b11aebda2023-11-23T19:38:03ZengMDPI AGApplied Sciences2076-34172022-06-011213651710.3390/app12136517Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix FeaturesLal Hussain0Hadeel Alsolai1Siwar Ben Haj Hassine2Mohamed K. Nour3Mesfer Al Duhayyim4Anwer Mustafa Hilal5Ahmed S. Salama6Abdelwahed Motwakel7Ishfaq Yaseen8Mohammed Rizwanullah9Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad 13100, PakistanDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, College of Science and Arts at Muhayel, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Computer Science, College of Computing and Information System, Umm Al-Qura University, Makkah 21955, Saudi ArabiaDepartment of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam Bin Abdulaziz University, Alfaj 16828, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaElectrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaIn the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.https://www.mdpi.com/2076-3417/12/13/6517GLCM features extractionimage enhancementmachine learningneural networkimage adjustment |
| spellingShingle | Lal Hussain Hadeel Alsolai Siwar Ben Haj Hassine Mohamed K. Nour Mesfer Al Duhayyim Anwer Mustafa Hilal Ahmed S. Salama Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features Applied Sciences GLCM features extraction image enhancement machine learning neural network image adjustment |
| title | Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features |
| title_full | Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features |
| title_fullStr | Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features |
| title_full_unstemmed | Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features |
| title_short | Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features |
| title_sort | lung cancer prediction using robust machine learning and image enhancement methods on extracted gray level co occurrence matrix features |
| topic | GLCM features extraction image enhancement machine learning neural network image adjustment |
| url | https://www.mdpi.com/2076-3417/12/13/6517 |
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